Bibliographic record
Abstract
Abstract Various terms have been bandied around over the past several decades for a wide array of heterodox medical systems, ranging from professionalized to folk medical systems. Within the US context, the term that has become commonplace in various circles is “complementary and alternative medicine,” whereas, for example, in Australia it is simply “complementary medicine.” At any rate, historically, medical sociologists have tended to focus on various aspects of biomedicine, including medical dominance and professionalism, and have tended to ignore alternative medical systems. Exceptions include the work of Walter Wardwell, Lesley Biggs, David Coulter, Ian Coulter, and Evan Willis on chiropractic in the United States, Canada, and Australia. Conversely, medical anthropologists have conducted studies of shamanism and other indigenous and folk medical systems as well as the phenomenon of medical pluralism in complex societies. In modern industrial or postindustrial societies, in addition to biomedicine – the dominant medical subsystem – one finds other medical subsystems such as homeopathy, osteopathy, chiropractic, naturopathy, religious healing systems, and popular and folk medical systems. Patterns of medical pluralism tend to reflect hierarchical relations in the larger society, including ones based upon class, caste, racial, ethnic, regional, religious, and gender divisions. The medical system of a complex society consists of the totality of medical subsystems that coexist in a generally competitive, but sometimes collaborative or even co‐optative, relationship with one another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.049 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".